CN117238042A - Vehicle bottom living body monitoring system - Google Patents
Vehicle bottom living body monitoring system Download PDFInfo
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- CN117238042A CN117238042A CN202311507980.XA CN202311507980A CN117238042A CN 117238042 A CN117238042 A CN 117238042A CN 202311507980 A CN202311507980 A CN 202311507980A CN 117238042 A CN117238042 A CN 117238042A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 128
- 241001465754 Metazoa Species 0.000 claims description 34
- 238000004140 cleaning Methods 0.000 claims description 29
- 230000005540 biological transmission Effects 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 9
- 230000004927 fusion Effects 0.000 claims description 8
- 238000007790 scraping Methods 0.000 claims description 8
- 238000010586 diagram Methods 0.000 description 12
- 238000012549 training Methods 0.000 description 5
- 238000013527 convolutional neural network Methods 0.000 description 3
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- 239000000758 substrate Substances 0.000 description 3
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- 230000036632 reaction speed Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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- 238000010801 machine learning Methods 0.000 description 1
- 230000009022 nonlinear effect Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
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Abstract
The invention relates to the technical field of vehicle monitoring, in particular to a vehicle bottom living body monitoring system, which monitors targets around a vehicle when the vehicle is in a parking state through a parking monitoring module, wherein the monitoring adopts a common camera or an infrared camera or a combined mode for monitoring, then the targets around the vehicle are detected through a driving monitoring module, and the driving speed is generally higher.
Description
Technical Field
The invention relates to the technical field of vehicle monitoring, in particular to a vehicle bottom living body monitoring system.
Background
Whether in cities or rural roads, there are situations where animals or humans are in the vicinity of the vehicle easily when the vehicle is parked or in the course of traveling. Although most drivers look around the vehicle for one week before departure, for a small portion of inexperienced drivers, no corresponding inspection is performed, which is likely to cause an accident.
The prior art provides a vehicle bottom living body monitoring method, a device, a terminal device and a storage medium (CN 116729261A), wherein the vehicle bottom living body monitoring method comprises the following steps: acquiring an underframe panoramic image of a vehicle, and determining a plurality of underframe objects; acquiring corresponding living body information according to the vehicle bottom object, wherein the living body information comprises data of a plurality of dimensions; judging whether the vehicle substrate belongs to a living body or not according to living body information; if the vehicle substrate belongs to a living body, triggering a preset living body early warning mechanism according to the vehicle substrate.
However, the above-mentioned method only indicates a general idea of living body detection, and the practicability is low when the complex road conditions are faced.
Disclosure of Invention
The invention aims to provide a vehicle bottom living body monitoring system, which aims to improve the capability of active intervention, reduce the occurrence of collision living bodies and the like and improve the driving safety.
In order to achieve the above purpose, the invention provides a vehicle bottom living body monitoring system, which comprises a parking monitoring module, a driving monitoring module, an identification module, a data transmission module and a warning module, wherein the parking monitoring module and the driving monitoring module are connected with the identification module, the data transmission module is connected with the identification module, and the warning module is connected with the data transmission module;
the parking monitoring module is used for monitoring targets near wheels in a parking state to obtain parking monitoring data;
the running monitoring module is used for monitoring a target in a running direction of the vehicle in a running state to obtain running monitoring data;
the identification module is used for identifying the target based on the parking monitoring data and the driving monitoring data to obtain identification data;
the data transmission module is used for synchronizing the identification data to the upper computer and the mobile terminal of the driver;
and the warning module is used for matching and warning the sound with the warning function of the target.
The parking monitoring module comprises a monitoring unit, a data fusion unit, a protection unit and a cleaning unit, wherein the monitoring unit is used for monitoring vehicle bottom data, the data fusion unit is used for fusing the monitored data to obtain fusion data, the protection unit is used for protecting the monitoring unit, and the cleaning unit is used for cleaning the protection unit when the data definition collected by the monitoring unit is reduced to a preset value.
The monitoring unit comprises a center monitor, a front monitor and a rear monitor, wherein the center monitor is mounted at the bottom of a vehicle to monitor the middle area of a front wheel and a rear wheel, the front monitor is used for monitoring the front area of the front wheel, and the rear monitor is used for monitoring the rear area of the rear wheel.
The cleaning unit comprises an air cylinder, a cleaning scraping blade and a reset spring, wherein the cleaning scraping blade is arranged on one side of the protection unit in a sliding mode, the output end of the air cylinder is fixedly connected with the cleaning scraping blade, and the reset spring is arranged between the air cylinder and the cleaning scraping blade.
The driving monitoring module comprises a remote monitoring unit, a distance estimation unit, a driving track prediction unit and a collision reminding unit, wherein the remote monitoring unit is used for collecting target information in front of driving of a vehicle, the distance estimation unit is used for estimating the distance between the position of the target and the current position of the vehicle, the driving track prediction unit is used for generating the driving track of the vehicle, and the collision reminding unit is used for generating collision possibility based on the distance and the driving track.
The running track prediction unit comprises a parameter acquisition unit and a track generation unit, wherein the parameter acquisition unit is used for acquiring running parameters in the running process of the vehicle, and the track generation unit is used for generating a moving track of the vehicle based on the running parameters.
The identification module comprises a modeling unit and a calling unit, wherein the modeling unit is used for building an animal prediction model based on animal image data, and the calling unit is used for calling the animal prediction model to identify animals based on monitoring data.
The vehicle bottom living body monitoring system further comprises an energy-saving module, wherein the energy-saving module is used for controlling the working modes of the parking monitoring module and the driving monitoring module.
According to the vehicle bottom living body monitoring system, the parking monitoring module can monitor the targets around the vehicle when the vehicle is in a parking state, wherein the monitoring can be performed by adopting a common camera or an infrared camera or a combined mode, then the traveling monitoring module can detect the targets around the vehicle in the traveling process, and because the traveling speed is generally high, in order to improve the reaction speed, the millimeter wave radar can be used for monitoring in a mode of matching with the camera, then the identification module is used for identifying animals based on collected data, so that relevant data of the animals are obtained, and then the data transmission module can be timely synchronized to a mobile terminal of a user for checking in advance to know the situation, and the warning module is further adopted for autonomously warning the possibly collided animals, so that the capability of active intervention can be improved, the occurrence of the situations of collision living bodies and the like can be reduced, and the traveling safety can be improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a block diagram of a living body monitoring system for a vehicle bottom according to a first embodiment of the present invention.
Fig. 2 is a structural view of a parking monitoring module according to a second embodiment of the present invention.
Fig. 3 is a structural diagram of a travel monitoring module according to a second embodiment of the present invention.
Fig. 4 is a block diagram of a travel locus prediction unit according to a second embodiment of the present invention.
Fig. 5 is a structural view of an identification module of a second embodiment of the present invention.
Fig. 6 is a block diagram of a data transmission module according to a second embodiment of the present invention.
Fig. 7 is a block diagram of a warning module according to a second embodiment of the present invention.
Fig. 8 is a structural view of an energy saving module according to a third embodiment of the present invention.
Parking monitoring module 101, travel monitoring module 102, recognition module 103, data transmission module 104, warning module 105, monitoring unit 201, data fusion unit 202, protection unit 203, cylinder 208, cleaning blade 209, return spring 210, remote monitoring unit 211, distance estimation unit 212, travel trajectory prediction unit 213, collision warning unit 214, parameter acquisition unit 215, trajectory generation unit 216, modeling unit 217, calling unit 218, data cleaning unit 219, data transmission unit 220, animal classification unit 221, sound library unit 222, matching unit 223, play unit 224, pattern library unit 302, pattern judgment unit 303, setting unit 304, switching unit 305.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
First embodiment
Referring to fig. 1, fig. 1 is a block diagram of a vehicle bottom living body monitoring system according to a first embodiment of the present invention.
The invention provides a vehicle bottom living body monitoring system, which comprises a parking monitoring module 101, a driving monitoring module 102, an identification module 103, a data transmission module 104 and a warning module 105, wherein the parking monitoring module 101 and the driving monitoring module 102 are connected with the identification module 103, the data transmission module 104 is connected with the identification module 103, and the warning module 105 is connected with the data transmission module 104; the parking monitoring module 101 is configured to monitor a target near a wheel in a parking state to obtain parking monitoring data; the running monitoring module 102 is configured to monitor a target in a running direction in a running state of the vehicle, so as to obtain running monitoring data; the identifying module 103 is configured to identify a target based on parking monitoring data and the driving monitoring data, so as to obtain identifying data; the data transmission module 104 is used for synchronizing the identification data to the upper computer and the mobile terminal of the driver; the warning module 105 is configured to match and warn a sound with a warning function on a target.
In this embodiment, the parking monitoring module 101 may monitor the target around the vehicle when the vehicle is in a parking state, where the monitoring may use a common camera or an infrared camera, or a combination manner, and then the driving monitoring module 102 may detect the target around the vehicle in the driving process, because the driving speed is generally faster, in order to improve the reaction speed, the method of matching the millimeter wave radar with the camera may be used for monitoring, and then the identification module 103 may identify the animal based on the collected data, so as to obtain relevant data of the animal, and then the data transmission module 104 may synchronize in time to the mobile terminal of the user to check in advance to know the situation, and further use the warning module 105 to autonomously warn the animal that may collide, so as to improve the capability of active intervention, reduce the occurrence of situations such as collision living body, and improve the driving safety.
Second embodiment
Referring to fig. 2 to 7, fig. 2 is a block diagram of a parking monitoring module according to a second embodiment of the present invention. Fig. 3 is a structural diagram of a travel monitoring module according to a second embodiment of the present invention. Fig. 4 is a block diagram of a travel locus prediction unit according to a second embodiment of the present invention. Fig. 5 is a structural view of an identification module of a second embodiment of the present invention. Fig. 6 is a block diagram of a data transmission module according to a second embodiment of the present invention. Fig. 7 is a block diagram of a warning module according to a second embodiment of the present invention. On the basis of the first embodiment, the present invention further provides a living body monitoring system for a vehicle bottom, where the parking monitoring module 101 includes a monitoring unit 201, a data fusion unit 202, a protection unit 203, and a cleaning unit, where the monitoring unit 201 is configured to monitor vehicle bottom data, the data fusion unit 202 is configured to fuse the monitored data to obtain fused data, the protection unit 203 is configured to protect the monitoring unit 201, and the cleaning unit is configured to clean the protection unit 203 when the sharpness of the data collected by the monitoring unit 201 is reduced to a preset value. By the mode, the environment around the vehicle can be conveniently monitored when the vehicle is parked.
The monitoring unit 201 includes a center monitor mounted to the bottom of the vehicle to monitor the middle area of the front and rear wheels, a front monitor for monitoring the front area of the front wheel, and a rear monitor for monitoring the rear area of the rear wheel. The shielding effect of the wheels can be avoided through the arrangement of the partitioned monitors, so that the monitoring is more comprehensive.
The cleaning unit comprises an air cylinder 208, a cleaning blade 209 and a return spring 210, wherein the cleaning blade 209 is arranged on one side of the protection unit 203 in a sliding manner, the output end of the air cylinder 208 is fixedly connected with the cleaning blade 209, and the return spring 210 is arranged between the air cylinder 208 and the cleaning blade 209. When the data definition is reduced, the cleaning blade 209 can be driven by the air cylinder 208 to move so as to clean the surface of the protection unit 203, and the cleaning blade 209 can be restored to the original position under the action of the return spring 210 after the cleaning is completed.
The driving monitoring module 102 comprises a remote monitoring unit 211, a distance estimation unit 212, a driving track prediction unit 213 and a collision reminding unit 214, wherein the remote monitoring unit 211 is used for collecting target information in front of driving of the vehicle, the distance estimation unit 212 is used for estimating the distance between the position of the target and the current position of the vehicle, the driving track prediction unit 213 is used for generating the driving track of the vehicle, and the collision reminding unit 214 is used for generating the collision possibility based on the distance and the driving track.
The travel locus prediction unit 213 includes a parameter acquisition unit 215 for acquiring a travel parameter during travel of the vehicle, and a locus generation unit 216 for generating a travel locus of the vehicle based on the travel parameter.
The identification module 103 comprises a modeling unit 217 and a calling unit 218, wherein the modeling unit 217 is used for establishing an animal prediction model based on animal image data, and the calling unit 218 is used for calling the animal prediction model to identify animals based on monitoring data. The specific modeling step of the modeling unit 217 is to collect a large amount of animal image data first. Such data can be obtained from public databases, such as ImageNet, or from photographs taken by themselves. The collected data is then pre-processed, including image cleaning (removing noise, resizing, etc.), image enhancement (rotation, flipping, brightness adjustment, etc.) and tag coding (coding the animal type to which each image corresponds as a number). A deep learning model, such as a Convolutional Neural Network (CNN), is then used to extract features of the image. CNNs are able to automatically learn and identify local features in an image and combine them into global features. The extracted features and corresponding labels are then used to train the model. During training, the model learns how to predict the animal type based on the image features. After training is completed, we need to verify and test the performance of the model. This is typically done by performing verification and testing on a separate data set. If the performance of the model is unsatisfactory, we can optimize the model by adjusting the parameters of the model, changing the network structure, or adding more training data.
The data transmission module 104 includes a data cleansing unit 219 and a data transmission unit 220, wherein the data cleansing unit 219 is configured to cleansing data generated by monitoring to remove invalid data and redundant data, and then transmit the processed data through the data transmission unit 220, so as to improve data transmission efficiency.
The warning module 105 includes an animal classification unit 221, a sound library unit 222, a matching unit 223, and a playing unit 224, where the animal classification unit 221 is configured to classify an identified animal, the sound library unit 222 is configured to obtain an animal sound and generate a database, the matching unit 223 is configured to match an animal category with a corresponding animal sound with a warning effect, and the playing unit 224 is configured to play the sound obtained by matching. The sound library unit 222 is established in the following manner: sound data was collected for various animals. Ready-made audio files may be downloaded from the internet or the animal's voice may be recorded by itself. Ensuring high audio quality for better identification of the animal type. The collected sound data is then preprocessed, including removing background noise, adjusting volume, etc. May be implemented using audio processing software or programming language, followed by extraction of useful features from the pre-processed sound data. These features may be a spectrogram, mel-frequency cepstral coefficient (MFCC), etc. May be implemented using audio processing software or a programming language; a classifier is trained using the extracted features and corresponding animal type tags. A machine learning algorithm such as a support vector machine, random forest, etc. may be selected. During training, the classifier learns how to predict the animal type based on the sound features. After training is completed, a separate data set is used to verify and test the performance of the classifier. This may be done by calculating the index of accuracy, recall, etc.
The playback unit 224 uses directional sound or directional speakers. The sound device utilizes the directional propagation characteristic of ultrasonic waves and the nonlinear effect of air, and the audio signal is modulated on an ultrasonic carrier wave to form the directional propagation of audible sound, so that the influence on non-targets can be avoided, and the pertinence is improved.
Third embodiment
Referring to fig. 8, fig. 8 is a block diagram of an energy saving module according to a third embodiment of the present invention. On the basis of the second embodiment, the invention further provides a vehicle bottom living body monitoring system, which further comprises an energy-saving module, wherein the energy-saving module is used for controlling the working modes of the parking monitoring module 101 and the driving monitoring module 102.
The energy saving module comprises a mode library unit 302, a mode judging unit 303, a setting unit 304 and a switching unit 305, wherein the mode library unit 302 is used for setting and storing a working mode, the mode judging unit 303 is used for judging the mode which should be currently in based on a mode parameter, the setting unit 304 is used for setting the working mode according to a judging result, and the switching unit 305 is used for switching the working mode when the condition is met. In the above manner, the parking monitoring module 101 and the travel monitoring module 102 can be operated at a suitable operating frequency to save energy.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (8)
1. A vehicle bottom living body monitoring system is characterized in that,
the system comprises a parking monitoring module, a driving monitoring module, an identification module, a data transmission module and a warning module, wherein the parking monitoring module and the driving monitoring module are connected with the identification module, the data transmission module is connected with the identification module, and the warning module is connected with the data transmission module;
the parking monitoring module is used for monitoring targets near wheels in a parking state to obtain parking monitoring data;
the running monitoring module is used for monitoring a target in a running direction of the vehicle in a running state to obtain running monitoring data;
the identification module is used for identifying the target based on the parking monitoring data and the driving monitoring data to obtain identification data;
the data transmission module is used for synchronizing the identification data to the upper computer and the mobile terminal of the driver;
and the warning module is used for matching and warning the sound with the warning function of the target.
2. The in-vehicle body monitoring system according to claim 1, wherein,
the parking monitoring module comprises a monitoring unit, a data fusion unit, a protection unit and a cleaning unit, wherein the monitoring unit is used for monitoring vehicle bottom data, the data fusion unit is used for fusing the monitored data to obtain fused data, the protection unit is used for protecting the monitoring unit, and the cleaning unit is used for cleaning the protection unit when the data definition acquired by the monitoring unit is reduced to a preset value.
3. A vehicle bottom living body monitoring system according to claim 2, wherein,
the monitoring unit comprises a center monitor, a front monitor and a rear monitor, wherein the center monitor is mounted at the bottom of a vehicle to monitor the middle area of front and rear wheels, the front monitor is used for monitoring the front area of the front wheels, and the rear monitor is used for monitoring the rear area of the rear wheels.
4. The in-vehicle body monitoring system according to claim 3, wherein,
the cleaning unit comprises a cylinder, a cleaning scraping blade and a reset spring, wherein the cleaning scraping blade is arranged on one side of the protection unit in a sliding mode, the output end of the cylinder is fixedly connected with the cleaning scraping blade, and the reset spring is arranged between the cylinder and the cleaning scraping blade.
5. The in-vehicle body monitoring system according to claim 4, wherein,
the driving monitoring module comprises a remote monitoring unit, a distance estimation unit, a driving track prediction unit and a collision reminding unit, wherein the remote monitoring unit is used for collecting target information in front of driving of a vehicle, the distance estimation unit is used for estimating the distance between the position of the target and the current position of the vehicle, the driving track prediction unit is used for generating the driving track of the vehicle, and the collision reminding unit is used for generating collision possibility based on the distance and the driving track.
6. The in-vehicle body monitoring system according to claim 5, wherein,
the travel track prediction unit comprises a parameter acquisition unit and a track generation unit, wherein the parameter acquisition unit is used for acquiring travel parameters in the process of traveling of the vehicle, and the track generation unit is used for generating a movement track of the vehicle based on the travel parameters.
7. The in-vehicle body monitoring system according to claim 6, wherein,
the identification module comprises a modeling unit and a calling unit, wherein the modeling unit is used for building an animal prediction model based on animal image data, and the calling unit is used for calling the animal prediction model to identify animals based on monitoring data.
8. The in-vehicle body monitoring system according to claim 7, wherein,
the vehicle bottom living body monitoring system further comprises an energy-saving module, wherein the energy-saving module is used for controlling the working modes of the parking monitoring module and the running monitoring module.
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Patent Citations (6)
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